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OpenCattle: Nationwide Mapping of Open Cattle Feedlots Using AI-Based Object Detection

What is OpenCattle?

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High-Res Aerial Image

Use of national coverage aerial images to efficiently identify the cattle feedlots

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AI object detection

Application of deep learning object detection with manually delineated feedlot areas

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Cattle feedlot inventory

Development of a cattle feedlot inventory with detailed spatial distribution.

Funded by

Geospatial and Environmental Epidemiology Research Unit (GEERU)

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Project scope

This project aims to develop and implement a high-precision, deep learning-based methodology to automatically detect open cattle feedlots across the contiguous United States using high-resolution NAIP imagery. The initiative addresses a critical data gap in confined animal area inventory by leveraging a state-of-the-art YOLO object detection model trained on thousands of hand-annotated examples.






Open cattle feedlots, vital to the U.S. livestock industry, are highly susceptible to extreme weather events that can severely impact animal welfare and productivity. Yet, comprehensive spatial data on their distribution remains scarce. This project enhances our ability to map these facilities at scale and lays the foundation for improved climate resilience planning, resource allocation, and policy-making in agricultural systems.

 

Accurate detection of animal areas across space is crucial for early disease outbreak identification, effective containment, and targeted prevention strategies to protect both animal and public health.

Research team

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Vitor Martins

Assistant Professor

Dept. of Ag and Bio Engineering

Mississippi State University

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Research focus:

Satellite remote sensing

Digital agriculture

Deep learning & HPC solution

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Uilson Aires

Postdoc Research Fellow

Dept. of Ag and Bio Engineering

Mississippi State University

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Research focus:

Remote sensing

Agriculture monitoring

Hydrology

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Contact Us

Dept. of Agricultural and Biological engineering
130 Creelman st
Mississippi State, MS 39762

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